A plethora of single-shot HDR works have been proposed [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. However, almost all existing methods have some significant drawbacks including ignoring reconstruction for under-exposed regions, neglecting dynamic range expansion for properly exposed pixels, or generating artifacts (i.e., color distortions, halo artifacts). An interesting recent development of imaging technology is a polarization camera that uses multi-directional on-chip linear polarizers to capture multi-polarization images in single-shot. Polarization is a property of light, which can convey rich illumination information about the environment to help with HDR reconstruction. Recent studies show that the polarization camera can handle the aforementioned drawbacks due to the acquisition of the different polarization filtered images. These polarization filtered images can capture different intensity values, where a region that is poorly exposed in one image may be properly exposed in another image. An existing polarimetric method by Wu et al. [7]} is a traditional model based approach, and is vulnerable to deal with a) poorly exposed pixels in polarized images, and b) pixels with low degree of polarization. Another existing polarimetric method by Ting et al. [8]} is a deep-learning-based approach, whose input consists of the four images from the polarization camera. However, where the degree of polarization is high, the method proposed by Ting et al. [8]} is unable to take advantage of the known physics of the polarization images as compared to the method by Wu et al. [7]}. Specifically, Ting et al. [8]} does not utilize degree of polarization to construct HDR images.
